Manufacturing process control with deep learning-based predictive model for hot metal temperature of blast furnace

ABSTRACT

A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/716,794 filed Sep. 27, 2017 which is incorporated by reference hereinin its entirety.

FIELD

The present application relates to an apparatus and control system for amanufacturing process.

BACKGROUND

A steel manufacturing process using a blast furnace is a complex,continuous operation that involves multiple chemical reactions and phasetransitions of materials. To better control the blast furnace during themanufacturing process for continuous production of quality metals, it isuseful to be able to predict state variables in the future timeassociated with the production such as the temperature of the hot metalproduced by the blast furnace. Generally, prediction algorithms utilizehistorical data for predicting the future data. However, in steelmanufacturing process involving the blast furnace, the state variablessuch as the hot metal temperature (also referred to as the pig irontemperature) are measured sparsely, for example, once in every few hoursat an irregular interval. Sparse, irregular measurement data makes itdifficult to be able to accurately predict the future data.

Other examples of continuous manufacturing processes include thealuminum smelting process, in which temperature of aluminum bath ismeasured once in two days, and the cement manufacturing processmeasuring fineness of cement particles once in an hour in a grindingstation.

BRIEF SUMMARY

A blast furnace control system and a method of controlling amanufacturing process in a blast furnace may be provided. The blastfurnace control system, in one aspect, may include a storage devicestoring a database of manufacturing process data associated with a blastfurnace. A hardware processor may be coupled to the storage device andoperable to receive the manufacturing process data, the manufacturingprocess data may include state variables and control variables used inoperating the blast furnace, the state variables comprising at least ahot metal temperature (HMT) and other state variables. The manufacturingprocess data may include a plurality of measured HMT at different timepoints, of a product continuously produced in the blast furnace. Thehardware processor may be further operable to generate imputed HMT byinterpolating the plurality of measured HMT. The hardware processor maybe further operable to generate HMT gradients over time at least basedon the imputed HMT. The hardware processor may be further operable todefine a causal relationship between the other state variables and theHMT gradients, the relationship generated as a neural network model. Thehardware processor may be further operable to train the neural networkmodel using as training data, a weighted combination of the imputed HMTup to last known measured HMT and predicted HMT up to the last knownmeasured HMT. The hardware processor may be further operable to run thetrained neural network model to predict a current point in time valuefor the HMT, in which no measured HMT for the current point in time isavailable, wherein the trained neural network model predicts the HMTcorresponding to a time period starting from the time of the lastmeasured HMT for a number of time periods until the number of timeperiods advances to the current point in time and use the predicted HMTcorresponding to each of the number of time periods to predict thecurrent point in time value for the HMT. The hardware processor may befurther operable to transmit the current point in time value for the HMTto a controller, the controller coupled to the blast furnace operable totrigger a control action to control a manufacturing process occurring inthe blast furnace.

A method of controlling a manufacturing process in a blast furnace, inone aspect, may include receiving manufacturing process data associatedwith a blast furnace. The manufacturing process data may include statevariables and control variables used in operating the blast furnace, thestate variables comprising at least a hot metal temperature (HMT) andother state variables. The manufacturing process data may include aplurality of measured HMT at different time points, of a productcontinuously produced in the blast furnace. The method may also includegenerating imputed HMT by interpolating the measured HMT. The method mayfurther include generating HMT gradients based on at least the imputedHMT. The method may also include defining a causal relationship betweenthe other state variables and the HMT gradients, the relationshipgenerated as a neural network model. The method further include trainingthe neural network model using as training data, a weighted combinationof the imputed HMT up to a last known measured HMT and predicted HMT upto the last known measured HMT. The method may further include runningthe trained neural network model to predict a current point in timevalue for the HMT, in which no measured HMT for the current point intime is available, wherein the trained neural network model predicts theHMT corresponding to a time period starting from the time of the lastmeasure HMT data point for a number of time periods until the number oftime periods advances to the current point in time and uses thepredicted HMT corresponding to each of the number of time periods topredict the current point in time value for the HMT. The method may alsoinclude transmitting the current point in time value for the HMT to acontroller coupled to the blast furnace, to trigger a control action tocontrol a manufacturing process occurring in the blast furnace.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a blast furnace in one embodiment.

FIG. 2 is a diagram illustrating components of a control system in oneembodiment.

FIG. 3 is a diagram that illustrates an example of sparse measurement ofa target variable as compared to other variables in one embodiment.

FIG. 4 shows a linear interpolation of HMT measurement data in oneembodiment.

FIGS. 5A and 5B illustrate HMT distribution and HMT difference (delta)distribution in one embodiment.

FIG. 6 is a diagram illustrating a method of controlling a manufacturingprocess occurring in a blast furnace in one embodiment of the presentdisclosure.

FIG. 7 shows a block diagram showing a deep neural network model forpredicting future HMT difference in one embodiment.

FIG. 8 is a diagram showing LSTM deep learning prediction model in oneembodiment.

FIG. 9 is a graphical diagram illustrating an example of forecasting ofa state variable ahead in time in one embodiment.

FIG. 10 illustrates Recurrent Neural Network (RNN) structure in oneembodiment.

FIG. 11 is a block diagram showing a memory cell of an LSTM network inone embodiment.

FIG. 12 is an architectural diagram showing an example long short-termmemory (LSTM) network in one embodiment.

FIG. 13 illustrates a schematic of an example computer or processingsystem that may implement a control system in one embodiment of thepresent disclosure.

DETAILED DESCRIPTION

A control system, apparatus, method and techniques are disclosed thatdevelop a deep learning (DL)-based predictive model for a manufacturingprocess, where available measurement data for state variables aresparse, to be able to control the manufacturing process. A predictivemodel in one embodiment predicts one or more state variables usingmachine learning (ML) or deep learning (DL). The DL-based predictivemodel in one embodiment is a data-driven model that is trained based onsparse observations (measurements) of one or more state variables.

In blast furnace operation, measurement data for state variables such asthe hot metal temperature data are available only sparsely. A DL-basedpredictive model in one embodiment predicts the hot metal temperature(HMT) of blast furnace operation with sparsely measured hot metaltemperature data.

For example, one or more of ML and DL techniques are employed to developa predictive model that can predict status of a manufacturing process, ablast furnace operation of steel manufacturing process. A blast furnaceinvolves a complex operation that includes multiple chemical reactionsand phase transitions of materials, which are difficult to model usingfirst principle equations. At the same time, because of the complexmulti-scale nature of the process, in which the response time of theinput materials, such as iron ore, coke, oxygen, water, pulverized coal(PC), have wide variations from order of minutes to hours, it isdifficult to develop a data-driven model in the conventionalmachine-learning approaches. In one embodiment of the presentdisclosure, a time-series prediction DL model, called Recurrent NeuralNetwork (RNN), is employed to build a predictive model. Particularly, anembodiment of the present disclosure may use the Long Short-Term Memory(LSTM) network, which is capable of learning multi-scale temporaldependency structures, to build models for predicting state variables(e.g., key state variables) of the blast furnace operation. The LSTM isable to capture complex non-linear dynamics well and is shown tooutperform conventional ML algorithms, such as Sparse Linear Model(LASSO), Decision Tree, Gradient Boosting, and Gaussian Processes, inthe prediction of blast furnace status.

FIG. 1 is a diagram illustrating a blast furnace in one embodiment. Ablast furnace 102 is a stack in which raw material such as iron ore andcoke are deposited into the top 104 and preheated air with moisture andoxygen content and pulverized coal (PC) are input into the bottom 106.The blast furnace is also equipped with sensors that surround the blastfurnace that measure various data, such as temperature and pressure, ofthe blast furnace in operation. A blast furnace in steel manufacturingprocess involves a complex, non-linear and continuous operation thatincludes multiple chemical reactions, phase transitions, and multiplephase interactions of materials. The process occurring in the blastfurnace is difficult to model using first principle equations. Changesin input materials (such as iron ore, coke, oxygen, pulverized coal)have delayed impact on the process and the product quality. Forinstance, it takes about 6-8 seconds for blasted air to react with thematerials and ascend to the top, and the raw materials or inputmaterials take about 6-8 hours to descend to the bottom of the furnace,and become the final product such as slag and pig iron.

The blast furnace is operated in extreme conditions (e.g., temperatureof approximately 2000 degrees Celsius, and atmospheric pressure ofapproximately 4 standard atmosphere (atm)), and the measurementcondition for internal blast furnace conditions is hostile. There may behundreds of process variables (e.g., temperature, pressure, raw materialcharge and exit) that are monitored and stored, for example, by thesensors. For instance, temperature sensors and pressure sensors may becoupled to or embedded on the surface of the blast furnace that measurethe temperature and pressure of the blast furnace at differentlocations. At the raw material charge and the exit of the tap hole,sensors may be coupled that measure the input and output rates. The hotmetal temperature (HMT) of the pig iron that is produced (output fromthe bottom of the blast furnace) is measured, for example, at intervalsof time.

The operation of the blast furnace consumes a large amount of energy andemits a large amount of carbon dioxide (CO₂). A control objective of theblast furnace iron-making process is to keep the operation close to theoptimal level, i.e., desired pig iron quality, low energy consumptionand high production. A goal, for example, is to achieve a stableoperation that achieves a desired blast furnace state and high qualitypig iron, at low energy cost. The desired blast furnace state, forinstance, includes balanced profiles of pressure and temperature,material (e.g., ore and coke) descending speed, gas permeability insideblast furnace, hot metal temperature, and Silicon (Si)/Phosphate(P)/Sulfate (S) content of pig iron. Ability to control the hot metaltemperature (HMT), also called pig iron temperature, to be maintainedapproximately at 1,500 degrees Celsius is also desirable.

FIG. 2 is a diagram illustrating components and processing flow of acontrol system in one embodiment. A computer or hardware processorcoupled to a blast furnace 202 (and/or to a control system that controlsthe blast furnace 202) may perform the processing described below.

A database 204 of process data stores the manufacturing process datareceived from the sensors coupled to the blast furnace. The process datastored in the database may include the temperature, pressure, rawmaterial charge rate, and air blow rate, measured periodically or atintervals of time. The process data other than the HMT is available morefrequently than the HMT data, as measurements for HMT are performed lessfrequently, i.e., sparsely. For example, HMT data may be measured every2-3 hours while other process data are measured every minute.

FIG. 3 is a diagram that illustrates an example of sparse measurement ofa target variable as compared to other variables. The target variable,for example, hot metal temperature (HMT) is measured at an irregularinterval (e.g., 2-4 hours), as shown by ‘x’ marks 304 in the graph 302,while the true state of HMT, as shown by dots 306 is not observed. Withsuch data, it is difficult to apply conventional time series models,which assumes that there exists a continuous observation, i.e., theconventional models require Y_(t) to predict Y_(t+1).

Referring to FIG. 2, the process data stored in the database 204 isreceived by a hardware processor. At 206, HMT data points are imputed byinterpolating HMT measurement data. Interpolating the HMT measurementdata produces additional data points between the actual measurement datafor the HMT. FIG. 4 shows a linear interpolation of HMT measurement datain one embodiment. The dots (e.g., 402) represent the actual measureddata. The data points (e.g., 404) between the dots are interpolateddata. For example, about 80 percent (%) of the training data maycomprise the linearly interpolated data and about 20% of the trainingdata may comprise the real observations (actual measured data).

Referring to FIG. 2, at 208, a Recurrent Neural Network (RNN) LongShort-Term Memory (LSTM) modeling of HMT gradient is performed. Forinstance, HMT gradients are computed from the interpolated HMT datapoints. The gradient is defined by the difference of HMT at successivetime steps, i.e., dY_(t+1)=Y_(t+1)−Y_(t). Here, d represents delta, Yrepresents a state variable such as the HMT, and t represents time unit.In this modeling process, neural network architecture is set up.Prediction of dY_(t+1), instead of Y_(t+1), prevents a development of ashort-circuit, in which RNN simply memorizes Y_(t) and copies it topredict Y_(t+1) as Y_(t+1)=Y_(t), instead of learning the dynamics.FIGS. 5A and 5B illustrate HMT distribution and HMT difference (delta)distribution. The HMT distribution shown in FIG. 5A is skewed to theleft, while the difference in HMT shown in FIG. 5B has a more symmetricdistribution. The gradient of HMT is modeled by LSTM, a deep neuralnetwork, for example, as shown in FIG. 6. As explained above, there isno explicit short-cut to the previous prediction, e.g., RNN cannot copythe input Yt to make a prediction. In one embodiment, the LSTM model hasa weighted L₂ loss function as follows:L=Σ _(i=1) ^(N)Σ_(j=1) ^(M)(|dY _(j) ^(i)|+δ)²(dY _(j) ^(i) −d{tildeover (Y)} _(j) ^(i))²,δ>0Here, N is the number of total time series, M is the length of a timeseries, dY^(i) _(j) denotes Y at the j-th time step for the i-th timeseries, {tilde over (Y)} is the RNN prediction, and δ is a parameter.The weighted L₂ loss function is devised to make the RNN prediction moreaccurate for larger changes in HMT, i.e., when dY is large. Forinstance, more than 20° C. may be considered to be large in blastfurnace operation. Another threshold value may be configured, forexample, above which is considered to be large.

Referring back to FIG. 2, at 210, the relationship between statevariables and hot metal temperature (HMT) data points is learned. Forexample, the RNN-LSTM model that is set up or generated at 208 istrained using the process data and the HMT data including theinterpolated HMT data. The RNN-LSTM model learns dY (difference HMT).

In one embodiment, predicting the HMT for future state includes guidedcruise of DL, autonomous prediction of unknown HMT using previouslypredicted HMT data, and blind forecasting of HMT.

At 212, guided cruise of DL using HMT measurement data and interpolatedHMT data is performed. In this processing step, LSTM is guided by themeasurement data:Y _(t+1) =w _(t+1) Y _(t+1)+(1−w _(t+1))Y* _(t+1)w _(t)=α[1−tan h(β(T−t))]Where Y*_(t+1) is the RNN-LSTM prediction, i.e., Y*_(t+1)=Y_(t)+d{tildeover (Y)}_(t+1), Y _(t+1) is interpolated value, and T is the time ofthe next measurement. The parameters, α and β, determine how close thereconstructed trajectory, Y_(t+1), should be to the linear interpolatedestimate, Y _(t+1). The first parameter, α∈(0,1), determine the relativeimportance between Y and Y*, and the second parameter, β, decides thetimescale of the weight function, e.g., for a large β, the weight isalmost zero in most of the time and becomes w=α very rapidly only aroundT, while a small β makes the weight, w, change more gradually in time.The parameters, α and β are configurable. Therefore, in one embodiment,more weight is placed on interpolated data closer to the measurement,while more weight is placed on the RNN-LSTM prediction away from themeasurement. In one embodiment, this guided cruise of DL is performedwith the past HMT data up to the last known HMT measurement. When (e.g.,responsive to, or after) a new HMT measurement is acquired, using thisnew data as a part of training data, the DL model is retrained.

At 214, autonomous prediction of unknown HMT using previously predictedHMT data is performed. Autonomous prediction allows for making aprediction without having the observation data at every time step. Inthis autonomous prediction process, the RNN-LSTM prediction in theprevious time step is used as input. For example, in the autonomousprediction mode, 1 time step prediction is performed as d{tilde over(Y)}_(T+n)=LSTM(Y*_(T+n−1),X_(T+n−1),U_(T+n−1)), in which X_(T+n−1) isthe observation of the process variables, U_(T+n−1) is the controlvariable, e.g., raw material charge rate, blast air volume, and blastair humidity, Y_(T) is the last HMT measurement, Y*_(T+n−1) is theRNN-LSTM prediction from Y_(T) computed recursively, e.g.,Y*_(T+1)=Y_(T)+LSTM(Y_(T),X_(T),U_(T)),Y*_(T+2)=Y*_(T+1)+LSTM(Y*_(T+1),X_(T+1),U_(T+1)), . . . ,Y*_(T+n−1)=Y*_(T+n−2)+LSTM(Y*_(T+n−2),X_(T+n−2),U_(T+n−2)). Theautonomous prediction is performed from the time of the last HMTmeasurement to the current time.

At 216, blind forecasting of HMT is performed. The blind forecasting isperformed to make a forecast from the current time. In this forecasting,a prediction is made for a future period, for example, n-time step aheador forward prediction such as a 1-hour ahead or forward prediction isperformed without providing any observation data. For example, supposethat the current time is 1:00 and the last HMT measurement time isT=0:00. Then, the autonomous prediction is made from 0:00 to the currenttime, 1:00, by using the observations of the process variables, X_(t),and the past control actions U_(t). As an example, it is assumed thatthe time step size is 20 minutes. Then, the current time can be denotedby T+3 and we have the autonomous model prediction,Y*_(T+3)=Y_(T)+Σ_(i=1) ³d{tilde over (Y)}_(T+i), and the observation ofprocess variables, X_(T+3). In the blind forecast mode, the HMTprediction is updated by fixing X to be the last known value and thefuture U according to a desired control strategy, i.e.,Y* _(T+4) =Y* _(T+3) +LSTM(Y* _(T+3) ,X _(T+3) ,U _(T+3)),Y* _(T+5) =Y*_(T+4) LSTM(Y* _(T+4) ,X _(T+3) ,U _(T+4)), and Y* _(T+6) =Y* _(T+5)+LSTM(Y* _(T+5) ,X _(T+3) ,U _(T+5)).

In one embodiment, retraining is performed for every new HMTmeasurement, i.e., the actual HMT data. For instance, receiving theactual HMT measured data may automatically trigger the retaining of themodel (e.g., the deep learning neural network model self-retraining orretraining itself, responsive to receiving an actual HMT measured data).

At 218, the forecasted HMT data at 216 is sent or transmitted to theprocess control system and/or to a control operator.

At 220, the control system in response to receiving the forecasted HMTdata performs a control action. Examples of the control action mayinclude adding humidity content or oxygen enrichment of blast air andincreasing iron ore to coke ratio, for instance, controlling the inputcontent amount by automatically controlling (closing or opening) aninput conduit.

FIG. 7 is a diagram showing an autonomous prediction in one embodiment.The prediction model, e.g., an LSTM network model 702 receives inputdata 704 associated with time t and outputs a prediction 706 of a deltavalue of the state variable for time t+1, for example, delta HMT at timet+1. The input data 704 is the actual measured data at time t. Thepredicted t+1 time data and process data measurements 710 are input tothe prediction model 708, and the model 708 outputs a prediction 712 ofa delta value of the state variable for time t+2, for example, delta HMTat time t+2. The LSTM model shown at 702 may be the same as the LSTMmodel shown at 708.

FIG. 8 is a diagram showing a blind forecast modeling in one embodiment.The figure illustrates a 3-step ahead or forward prediction as anexample; for example, if 20-minute time interval is used for each timestep, 3-step ahead prediction produces a forecast or prediction for1-hour ahead of time t. For instance,Y_(t+1hour)=Y_(t)+dY*_(t+1)+dY*_(t+2)+dY*_(t+3), where dY*_(t+1),dY*_(t+2), and dY*_(t+3) are predicted delta values, and Y_(t) is theactual measured data. The model runs are performed sequentially, forexample, using as input a previously predicted output at the previousfuture time step run. Y here represents the state variable beingpredicted, e.g., HMT. X here represents other features or variables usedby the model, such as the temperature and pressure measurements fromsensors. U here represents the control actions, such as the raw materialcharging rate, blast air humidity, blast air volume, and so on. Themodel learns the relationship between Y, X, and U values.

FIG. 9 is a graphical diagram illustrating an example of forecasting ofa state variable (e.g., HMT) ahead in time in one embodiment. The pointin time shown at 1002 represents the time at the last HMT measurement.The point in time shown at 1004 represents the current time, forexample, the time to make a 1-hour ahead or forward prediction. At thetime shown at 1002, when that HMT measurement is received, theprediction model (e.g., LSTM model) is retrained by guided cruisetechnique described above. The LSTM model is guided by the measurementdata, in that the interpolated data (shown by data points along the line1010) between that last measurement data 1006 and the previousmeasurement data 1008 are generated and combined with the previouslypredicted data (predicted data by autonomous prediction shown along thecurve 1012) to retrain the model. In one embodiment, the training datafrom the time of the measured data point at 1008 to the time of themeasured data point at 1006, are generated as the weighted average orweighted combination of the interpolated data 1010 and the predicteddata 1012 between those two measured data points.

From the time 1002 of the last HMT measurement, an autonomous predictionmode takes place where 1-time step prediction is performed without aforward HMT measured data. For instance, self-generated HMT is used asinput to LSTM model to generate a next time step prediction. To generatea prediction data for the current time shown at 1004, a blind forecastmode is performed. The blind forecast mode performs the 1-stepprediction of the autonomous prediction mode, n-times to forward to thecurrent time. For instance, if the time duration between the time at thelast HMT measurement 1002 and the current time 104 is 1 hour, and if the1-step time is 20 minutes, then 3-step predictions are performed, forinstance, as shown in FIG. 8. In performing the blind forecast mode, inone embodiment, the values of other features used in the model, such aspressure and temperature observations from sensors, may be set to thelast known values.

In one embodiment, the long short-term memory (LSTM) network is modeled.The LSTM model of the present disclosure in one embodiment mitigatesproblems of rapidly degrading accuracy as the time lag increases, andbeing able to account for the trajectory of a dynamical system, whichmay occur in other learning algorithms. The LSTM model of the presentdisclosure in one embodiment is a latent space model that incorporatesthe past trajectory of a blast furnace, and provides for the continuousestimation of the current state of the blast furnace and prediction forthe future.

Deep learning (DL), a type of machine learning, is a computational modelcomposed of multiple processing layers to learn representations of datawith multiple levels of abstraction. Deep learning methods have beenutilized in speech recognition, visual object recognition, objectdetection and other domains such as drug discovery and genomics. Arecurrent neural network (RNN) is a type of neural network in which theneuron feeds back information to itself, in addition to processing tothe next neuron, and is capable of learning long-term dependencies,e.g., time-series data. An RNN can learn long-term dependencies, but hasdifficulties in learning to store information for a long duration. Longshort-term memory (LSTM) networks augment the RNN network with anexplicit memory through a built-in memory cell. In the presentdisclosure, LSTM technology is utilized to develop a predictive modelfor a complex manufacturing process, providing for an LSTM algorithmand/or architecture for discovering a long term dependency in theprocess.

FIG. 10 illustrates RNN in one embodiment. The left side 1102 of FIG. 10shows the structure of a RNN. For a specific time period of modeling, aRNN model captures the relationship, called state s, between input x,output o and the state s in previous time period. The parameter set forthe links between x and s denotes U, and the parameter set for the linksbetween s and o denotes V. The parameter set for the links betweenprevious state and current state denotes W. If the RNN structure isunfolded to show the relationship between states in different timeperiods, it looks like the structure 1104 on right side of FIG. 10. Thestate at time t+1, s_(t+1), captures the relationship between the stateat time t, s_(t), input x_(t+1) and output o_(t+1).

FIG. 11 is a block diagram showing a memory cell of an LSTM network inone embodiment. Since an LSTM uses data from previous time steps, theamount of data used by a LSTM model may be very large. In order tohandle the data size problem in a computer system, the memory cellsscreen the amount of data to be used by controlling three types ofgates. An input gate conditionally determines which input data to use inthe LSTM model. A forget gate conditionally determines which information(data) it learned from past time periods is going to be used in thememory cell of current time step. An output gate conditionallydetermines which data it is currently using in the memory cell, tooutput to the memory cell of next time period.

FIG. 12 is an architectural diagram showing an example long short-termmemory (LSTM) network in one embodiment. Here, Y_(t), e.g., 1202,denotes a vector of the target variables at time t. X_(t) and U_(t) arethe state variables and the control variables at time t. At each timestep t, LSTM updates its memory cell, e.g., 1206 (example shown in FIG.11), and makes a prediction at the next time step, Y_(t+1), e.g., 1204,from the current observations Y_(t), state variables, X_(t), and thecontrol actions, U_(t), e.g., 1208. Because the past information isstored in the memory cells, the previous observations, e.g., Y_(t−1),Y_(t−2), X_(t−1), X_(t−2), U_(t−1), U_(t−2), are not used in theprediction at time t.

A long short-term memory (LSTM) model in one embodiment is a recurrentneural network (RNN). Information learned is passed from one step of thenetwork to the next step. The long short-term memory (LSTM) modelconnects previous information to the present state, and learns long-termdependencies (time-series data). A response time of control actions haswide variations, e.g., from a few seconds to hours. For instance, thestate variables that change as a consequence of the control actionsperformed on the blast furnace, may be reflected in data sensed fewseconds to hours after the time of the control action.

The LSTM model in one embodiment predicts a future state for a statevariable as a function of the previous states of the state variable andother variables involved in the process. For instance, Y_(t+1)=ƒ(y_(t),y_(t−1), y_(t−2), . . . , x_(t), x_(t−1), x_(t−2), . . . , u_(t),u_(t−1), u_(t−2), . . . , u*_(t+1)), where t represents a point in time(time unit), y represents the response variable (also called targetvariables) that are intended to be predicted, x representsuncontrollable state variables (also called observed variables), and urepresents controllable state variables (also called control variables).

The future state of a state variable is determined, for example, asfollows:y _(t+1)=ƒ(Y _(t) ⁻ ,X _(t) ⁻ ,U _(t) ⁻ ,U _(t+1) ⁺),where t represents point in time, y represents the state variable whosefuture state is being predicted, Y_(t) ⁻ represents a vector of responsevariables from past to present time, X_(t) ⁻ represents a vector ofuncontrollable state variables from past to present time, U_(t) ⁻represents a vector of controllable state variables from past to presenttime, and U_(t+1) ⁺ represents a vector of controllable state variablefor the future time,wheretarget variable (y): Y_(t) ⁻={y_(t), y_(t−1), . . . y_(t−n)}, nrepresenting number of past time steps,state variables: X_(t) ⁻={x_(t), x_(t−1), . . . x_(t−n)},control variables (past): U_(t) ⁻={u_(t), u_(t−1), . . . u_(t−n)}control variables (future): U_(t+1) ⁺={u_(t+1), u_(t+2), . . . u_(t+M)},M representing number of future time steps for prediction.

FIG. 6 is a diagram illustrating a method of controlling a manufacturingprocess occurring in a blast furnace in one embodiment of the presentdisclosure. At 602, manufacturing process data associated with a blastfurnace is received. The manufacturing process data includes statevariables and control variables used in operating the blast furnace, thestate variables comprising at least a hot metal temperature (HMT) andother state variables, wherein the manufacturing process data comprisesa plurality of measured HMT at different time points, of a productcontinuously produced in the blast furnace. At 604, imputed HMT isgenerated by interpolating the measured HMT. For instance, HMT isimputed that correspond to the time points between the times of themeasure HMTs. At 606, HMT gradients are generated based at least on theimputed HMT. For instance, the difference between an imputed HMT at atime point and the next imputed HMT at the next time point is determinedover a plurality of time points as a time series data. At 608, a causalrelationship is defined between the other state variables and the HMTgradients. The relationship is generated as a neural network model. At610, the neural network model is trained using as training data, aweighted combination of the imputed HMT up to a last known measured HMTand predicted HMT up to the last known measured HMT. At 612, the trainedneural network model is run to predict a current point in time value forthe HMT, in which no measured HMT for the current point in time isavailable. The trained neural network model predicts the HMTcorresponding to a time period starting from the time of the lastmeasure HMT data point for a number of time periods until the number oftime periods advances to the current point in time and uses thepredicted HMT corresponding to each of the number of time periods topredict the current point in time value for the HMT. At 614, the currentpoint in time value for the HMT is transmitted to a controller coupledto the blast furnace, to trigger a control action to control amanufacturing process occurring in the blast furnace. The product, forexample, includes pig iron and the manufacturing process includes acontinuous blast furnace operation. In one embodiment, the neuralnetwork model includes a long short-term memory network. Themanufacturing process data may be stored as a time series data. In oneembodiment, the neural network model is autonomously retrainedresponsive to receiving a new measured HMT, using the new measured HMTas the last known measured HMT. In one embodiment, the plurality ofmeasured HMT at different time points includes a plurality of measuredHMT measured at irregular time intervals.

FIG. 13 illustrates a schematic of an example computer or processingsystem that may implement a control system in one embodiment of thepresent disclosure. The computer system is only one example of asuitable processing system and is not intended to suggest any limitationas to the scope of use or functionality of embodiments of themethodology described herein. The processing system shown may beoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with the processing system shown in FIG. 13 mayinclude, but are not limited to, personal computer systems, servercomputer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a predictive model module30 that performs the methods described herein. The module 30 may beprogrammed into the integrated circuits of the processor 12, or loadedfrom memory 16, storage device 18, or network 24 or combinationsthereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

We claim:
 1. A method of controlling a manufacturing process in a blastfurnace, the method executed by at least one hardware processor, themethod comprising: receiving manufacturing process data associated witha blast furnace, the manufacturing process data comprising statevariables and control variables used in operating the blast furnace, thestate variables comprising at least a hot metal temperature (HMT) andother state variables, wherein the manufacturing process data comprisesa plurality of measured HMT at different time points, of a productcontinuously produced in the blast furnace; generating imputed HMT byinterpolating the measured HMT; generating HMT gradients based on atleast the imputed HMT; defining a causal relationship between the otherstate variables and the HMT gradients, the relationship generated as aneural network model; training the neural network model using astraining data, a weighted combination of the imputed HMT up to a lastknown measured HMT and predicted HMT up to the last known measured HMT;running the trained neural network model to predict a current point intime value for the HMT, in which no measured HMT for the current pointin time is available, wherein the trained neural network model predictsthe HMT corresponding to a time period starting from the time of thelast measure HMT data point for a number of time periods until thenumber of time periods advances to the current point in time and usesthe predicted HMT corresponding to each of the number of time periods topredict the current point in time value for the HMT; and transmittingthe current point in time value for the HMT to a controller to trigger acontrol action to control a manufacturing process occurring in the blastfurnace, the control action including at least selectively opening aconduit to the blast furnace to control content amount of input to theblast furnace.
 2. The method of claim 1, wherein the product comprisespig iron.
 3. The method of claim 1, wherein the neural network modelcomprises a long short-term memory network.
 4. The method of claim 1,wherein the manufacturing process data is stored as a time series data.5. The method of claim 1, further comprising autonomously retraining theneural network model responsive to receiving a new measured HMT, usingthe new measured HMT as the last known measured HMT.
 6. The method ofclaim 1, wherein the manufacturing process includes a continuous blastfurnace operation.
 7. The method of claim 1, wherein the plurality ofmeasured HMT at different time points comprises a plurality of measuredHMT measured at irregular time intervals.